Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.


Supplementary Note 2: Comparing icSHAPE with other in vivo and computationally predicted secondary structure profiles
To demonstrate the effectiveness of icSHAPE in our HDRNet model further, we conducted an experiment to compare ic-SHAPE with other RNA secondary structure representation models, namely RNAfold (9) and two in-vivo secondary structural characterization methods: DMS-seq (10) and DMS-MaPSeq (11).To ensure a fair comparison, we kept all other modules of HDRNet, substituting only icSHAPE with the structural features derived from the comparative methods.The experimental results are summarized in Supplementary Fig. 2. As depicted in Supplementary Fig. 2a, the HDRNet model with icSHAPE for structural representation outperformed HDRNet models with other structural features in identifying protein-RNA interactions.We speculate that computationally predicted secondary structures could be obsolete and hence prone to artifacts.In contrast, in-vivo secondary structure scores, representing the probabilities of nucleotide pairing, could potentially offer rich structural information.Taking the SLTM protein dataset from K562 cells as an example, we subsequently utilized t-SNE to visualize the embedding representation of HDRNet with various structural information.As illustrated in Supplementary Fig. 2b, icSHAPE demonstrated a distinct division between positive and negative samples, with each group clearly situated on opposite sides.However, for DMS-seq and DMS-MaPseq, positive and negative samples are considerably overlapped between the clusters, whereas the performance of RNAfold was deemed suboptimal due to insufficient sample separation.

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Supplementary Fig. 2c provides a visual representation of the high-attention binding regions identified by different RNA secondary structure representation models.Notably, icSHAPE and RNAfold, when used as the structural features within the HDRNet model, successfully captured continuous high-attention regions.Conversely, DMS-seq and DMS-MaPseq exhibited a considerable number of missing positions, resulting in a lack of continuity in the regions they identified.Moreover, the RNA models with those secondary structure features exhibited significant differences; for instance, icSHAPE could predict RNA models with greater structural heterogeneity and a higher number of nucleotide pairings.Conversely, RNAfold structures resembled those constrained using DMS-MaPseq, suggesting that the performance of DMS-MaPseq in structure prediction may be vulnerable to of missing positions.On this basis, we can conclude that these analyses not only highlight the effectiveness of icSHAPE features but also underscores potential limitations.Since the acquisition of icSHAPE data relies on sequencing experiments, RNAfold or other secondary structure prediction algorithms may serve as suitable alternatives for general tasks.These observations emphasize the importance of carefully selecting the most appropriate RNA secondary structure representation model based on the specific requirements of the task at hand.We also conducted further experiments to investigate and compare the performance of HDRNet by integrating RNA structure information from different methods.Specifically, we explored multiple secondary structure features obtained from different methods and then employed a concatenation strategy to integrate them into the HDRNet model.As demonstrated in Supplementary Fig. 3, our observations indicate that the performance of HDRNet was not further improved but slightly decreased by integrating other secondary structure features.This observation indicates that those in vivo secondary structure information provides a more accurate depiction of the RNA environment than the computationally predicted secondary structure information (RNAfold), thus ensuring the performance of HDRNet.These results support the reliability of HDRNet and suggest that the original HDRNet is sufficiently robust as a standalone model.To demonstrate the effectiveness of our chosen network architecture, we initially constructed a non-hierarchical HDRNet, referred to as HDRNet-nonhier, which combined multi-source features at the network's inception.After that, a comparative analysis was conducted between our proposed HDRNet and HDRNet-nonhier, evaluating their performance in both static and dynamic prediction tasks.The comparative results are summarized in Supplementary Fig. 6a, revealing that the HDRNet, characterized by its hierarchical structure, exhibited significantly superior prediction performance compared to its non-hierarchical counterpart (HDRNet-nonhier).Moreover, HDRNet, with its hierarchical architecture, demonstrated superior performance in intercellular dynamic prediction.Furthermore, we performed feature correlation analysis and visualization across different versions of HDRNet.For illustration purpose, we employed the TBRG4 protein dataset in HepG2 cells as a representative sample, as depicted in Supplementary Fig. 6b.We can observe that HDRNet with a hierarchical structure has a more distinct feature correlation and hierarchy than the non-hierarchical one.Therefore, sequence information and secondary structure profiles can be normalized and fused, enriching the features learned by HDRNet to be robust.

Supplementary Note 5: Existing sub-groups of binding events that are better characterized by HDRNet
In this part, we conducted a comparison between HDRNet and PrismNet, focusing on their performance across various cell lines, and the detailed results of this comparison are presented in Supplementary Fig. 7a.As demonstrated in this figure, we can observe that HDRNet was consistently superior to PrismNet in all cell lines evaluated, with significant enhancement observed in HEK293, HepG2, and K562 cells.To ensure the credibility of our findings, we selected datasets from these three cell lines in which HDRNet was at least 5% better than PrismNet.We then analyzed the performance gap between the proposed HDRNet and PrismNet models for these datasets, as demonstrated in Supplementary Fig. 7b.We found that HDRNet showed a more significant performance improvement in predicting multiple datasets in HEK293 cell line.For illustration purpose, we further explored those RBPs in HEK293 cell lines.To investigate it, we have mapped these RBPs into STRING (12) and then used the the Markov Cluster Algorithm (MCL) with an inflation rate of 2.5 to cluster them, as demonstrated in Supplementary Fig. 7c.As can be seen from the figure, RBPs with similar biological functions are clustered together, while RBP datasets exhibiting significant performance improvements are consistently grouped into the same functional clusters, e.g., 15% for FMR2 and 13% for FXR1, in the subclusters represented by the red nodes highlighted.Moreover, in this cluster, previous studies have demonstrated that several RBPs (e.g., FXR2, FMR1, LIN28B) have the coexpression in the same tissues (13), which further indicates that these RBPs in the cluster may have similar context or structures.
With the distinct subgroups identified, we proceeded with an analysis of the structural complexity of these RBPs.Initially, we utilized HDRNet to scan the RBP datasets and extracted the high-attention 6-mer fragments that HDRNet identified most frequently.Similar to PrismNet, we then calculated the structural complexity of these 6-mer fragments, generated the PWM matrix and represented their structural motifs where the structure component using the labels "U" for unpaired nucleotide and "P" for paired.As shown in Supplementary Fig. 7d, we observe that RBPs in the same subgroup show structural similarities.For instance, the subgroup of NUDT21, CPSF2, and CPSF4 exhibited a prevalent preference for binding to paired structures, whereas RBPs in the subgroup containing LIN28A demonstrated a higher tendency to interact with complex structure fragments.Meanwhile, the binding region of FBL usually does not pair.These phenomena demonstrate the potential structural differences between different subgroups.
To investigate context variations further, we explored the context variations between RBP clusters using 3-mer analysis, tailored to the tokenization used by our dynamic global contextual embedding approach.In specific, we calculated the relative content of each 3-mer token within each RBP dataset.Subsequently, we employed hierarchical clustering to group RBPs based on their 3-mer token content, thereby assigning RBPs with similar profiles to the same cluster.As shown in Supplementary Fig. 7e, it can be seen that the results are in close agreement with the clusters we obtained through the STRING database; for instance, RBPs such as FMR1, FXR2, LIN28A, and LIN28B were consistently assigned to the same cluster, while CPSF2 and CPSF4 were in a distinct cluster.These findings further support the notion that the 3-mer token with contextual information can serve as an informative feature to identify different sub-groups of RBPs.Supplementary Note 7: HDRNet has superior performance for RBPs with high and low expression levels and for target RNA events with high and low expression levels in different cellular contexts.
To observe the validity of HDRNet across various expression levels of RBPs in both the same and different cellular contexts, we utilized the expression levels of RBPs in HepG2 and K562 cell lines from the reference (19) published in Nature, which provides a comprehensive collection of human RBPs in K562 and HepG2 cells from the Encyclopedia of DNA Elements (ENCODE) project phase III, including the expression levels of RBPs across different cell lines within the eCLIP dataset.To distinguish between high and low expression levels of RBPs, we employed the average expression level as the threshold.RBPs with expression levels surpassing this threshold were classified as highly expressed, whereas those below were considered lowly expressed.On this basis, we compared the performance of HDRNet with baseline methods separately on K562 and HepG2 cell lines.As illustrated in Supplementary Fig. 9a, we observed that HDRNet exhibited superior static prediction performance for RBPs with varying expression levels in different cell lines.Furthermore, we noted a slight improvement in HDRNet's performance on RBPs with higher expression levels compared to those with lower expression levels within the same cell line.Meanwhile, to further investigate the relationship between RBP expression levels and the performance of HDRNet, we calculated their correlation.As depicted in Supplementary Fig. 9b, we found a weak positive correlation between the performance of HDRNet, as measured by metrics such as the AUC and RBP expression levels, suggesting that the binding sites of RBPs with higher expression levels were more likely to be accurately recognized by HDRNet.
Then, we analyzed the relationship between the performance of HDRNet and RBP expression levels from the perspective of dynamic prediction.As demonstrated in Supplementary Fig. 9c, we first computed the correlation of RBP expression levels in the K562 and HepG2 cell lines.We found a strong positive correlation between the RBP expression levels in these two cell lines, indicating similar RBP expression patterns between them.Therefore, we evaluated the dynamic prediction performance of HDRNet based on these categorizations.As depicted in Supplementary Fig. 9d, consistent with the static prediction results, HDRNet exhibited the most accurate dynamic predictive performance for these RBPs.However, it is worth noting that there are differentially expressed RBPs between the two cell lines.To identify these differentially expressed RBPs, we employed the DESeq2 package, as described in the reference (19).A total of 35 RBPs were identified, of which 15 were differentially expressed RBPs in K562 cells, and 20 were differentially expressed RBPs in HepG2 cells.Supplementary Fig. 9e visualizes the performance of HDRNet on these differentially expressed RBPs, confirming its superior performance in both cases.Based on these results, we further investigated whether the expression levels of RBPs influenced the dynamic prediction performance of HDRNet.As displayed in Supplementary Fig. 9f, we observed that the binding sites of RBPs with higher expression levels were more easily identified during the dynamic prediction process.
On the other hand, we conducted further analysis to evaluate the performance of HDRNet on RBPs with different target expression levels.RBPs play a crucial role in regulating gene expression by interacting with RNA and forming ribonucleoprotein complexes.However, the ENCODE project data (19) for RBPs in K562 and HepG2 cells does not provide expression values for their target RNAs, making it challenging to directly measure the high and low expression levels of target RNA events.Nevertheless, they have employed RNA sequencing after depleting RBPs using short hairpin RNA (shRNA) or CRISPR to investigate RBP target binding site functionality and examined expression level differences before and after knockdown (19).On this basis, there are 4 RBPs including DD3X3, DDX6, LARP4 and RBM15 that have previously been identified as RNA decay factors.After RBP-knockdown experiments, their target genes exhibited upregulation in specific cell conditions (knockdown-increased), indicating that these RBPs are associated with low expression levels of their target genes.Similarly, there are 6 RBPs including AKAP1, DDX55, APOBEC3C, FMR1, CPSF6, and IGF2BP3 that have been previously recognized to increase the stability of RNA targets; and their target genes showed downregulation after RBP knockdown in specific cell contexts (knockdowndecreased), indicating that these RBPs are associated with higher expression levels of their target genes.With these target RNA events with high and low expression levels, we first examined the static predictive performance of HDRNet on the specific cell line data for these RBPs.As depicted in Supplementary Fig. 10a,b, HDRNet consistently achieved the best performance and showed significant improvements compared to other baseline methods.For instance, in the RBM15_K562 dataset, HDRNet shows its performance gain of 8% over PrismNet; in the FMR1_K562 dataset, only HDRNet's AUC metric exceeded 0.9.These results indicate that HDRNet is capable of accommodating RBPs with different target expression levels and effectively identifying their binding characteristics.Furthermore, we selected those RBPs within this cohort that allowed for dynamic prediction and compared HDRNet's dynamic predictive performance.As demonstrated in Supplementary Fig. 10c,d, HDRNet also exhibited superior performance in dynamic prediction as well.For example, we observed a performance improvement of over 10% for RBM15 when comparing HDRNet to PrismNet.Notably, other baseline methods displayed considerable instability in the dynamic prediction tasks; for instance, DMSK failed to accurately identify the binding sites of DDX3X, and PrismNet performed poorly on AKAP1, with an AUC below 0.7.These results further validate the adaptability and robustness of HDRNet to RBPs with different target expression levels.

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Supplementary Note 8: HDRNet predicts dynamic RNA-RBP interactions on in vivo tissues under normal and disease conditions.
To demonstrate the effectiveness of HDRNet in both normal and disease conditions, we have meticulously curated the MBNL2 (Muscleblind Like Splicing Regulator 2) binding peak data (20) (GEO accession: GSE68890) in human brain tissues from POSTAR (21).This data source is comprised of five distinct datasets, including autopsy tissues (hippocampus and frontal cortex) from the patients with myotonic dystrophy type 1 (DM1, 2 datasets), myotonic dystrophy type 2 (DM2, 1 dataset of hippocampus), and control subjects (2 datasets), where DM1 and DM2 are progressive and multi-systemic neuromus-cular disorders, originating from the aberrant sequestration and activation of RNA processing factors and RAN translation.Then, we consolidated replicate data within each dataset and standardized the binding peaks to a length of 101 nucleotides.Subsequent evaluations of the dynamic prediction performance on these tissue datasets involved HDRNet, along with baseline models including PrismNet, DMSK, iDeep, GraphProt, DeepBind, and PRIESSTESS.Firstly, the evaluations focused on cross-tissue dynamic prediction experiments under single conditions, such as normal-normal and DM1-DM1 predictions.As shown in Fig. 4a, HDRNet consistently outperformed the other models across both conditions.Notably, we observed a significant performance gap between HDRNet and PrismNet in the cross-tissue prediction task, while PRIESSTESS failed to identify the binding sites of MBNL2 in the DM1 frontal cortex dataset, rendering it incapable of performing the dynamic prediction task.Then, to validate the efficacy of HDRNet in capturing dynamic tissue conditions between normal and diseases, we conducted experiments on cross-condition dynamic predictions, such as, we used the model trained on normal control tissue data to predict binding sites in disease tissues.As shown in Fig. 4b, HDRNet maintained strong dynamic prediction capabilities in cross-condition dynamic prediction.It achieved the highest AUC metric of 0.8, with an AUC difference exceeding 10% compared to PrismNet.These results demonstrate that HDRNet can effectively learn the binding patterns of RBPs across diverse conditions.Additionally, as highlighted in Fig. 4c and Supplementary Fig. 11a, HDRNet exhibited a robust capacity to detect and accurately capture disease-related high-attention binding regions.These regions represent the critical interaction sites between MBNL2 and DM1 extended CUG repeats and DM2 CCUG expansion RNAs, which are pathophysiological hallmarks of myotonic dystrophies, as previously revealed in (20,22).By successfully capturing these regions, HDRNet not only aids in identifying key molecular interactions but also enhances our understanding of disease mechanisms.
To further investigate the predictive capabilities of our proposed model regarding dynamic interactions in tissues, we conducted an experiment to predict two additional eCLIP RBP datasets from adrenal tissues obtained from ENCODE, namely DGCR8 and HNRNPU.The experiment results are summarized in Supplementary Fig. 11b, where our model achieved the highest AUC metric.As depicted in this figure, we can observe that HDRNet achieved identical performance in dynamically predicting RBP binding in both K562 and HepG2 cell lines, while other baseline methods exhibited variability in performance when using models trained on different cell lines for dynamic prediction.This observation further highlights the ability of HDRNet to identify RBP binding patterns across different sources of RBP binding data.Interestingly, we also observed that PrismNet, although slightly inferior to HDRNet, showed improved performance on the new eCLIP data compared to the previous MBNL2 data.We speculate that this improvement is due to the fact that the PrismNet model was specifically designed based on eCLIP data.In particular, the static encoding within PrismNet limits its performance on other platforms.In addition, our observations revealed that the HDRNet model, trained on cell line data, successfully highlighted significant binding regions in the tissue binding data which were functionally relevant; for example, focusing on the DGCR8 protein, it has been reported to bind to extended CGG repeat sequences, leading to partial sequestration of DGCR8 in CGG RNA aggregates, consequently reducing the processing of miRNAs (23).In Supplementary Fig. 11c, we can observe that HDRNet effectively identifies the dynamic CGG repeat binding region, indicating its capability to dynamically identify RBP binding patterns.Additionally, HDRNet accurately identified the G-rich binding region of HNRNPU, as depicted in Fig. 4d.Notably, upon visualizing potential RNA models, we discovered that the identified G-rich binding regions predominantly corresponded to G-quadruplex structures, which are stable secondary structures formed by G-G base pairs.Indeed, as reported by (24), HNRNPU exhibits a preference for recognizing G-quadruplexes in RNAs as binding motifs.These results highlight the robustness of HDRNet on RBP data across different platforms, demonstrating that HDRNet with K562 and HepG2 cell line data is sufficient to accurately predict dynamic interactions in tissues.
Lastly, we have also added the direct regulatory targets of MBNL1

1 .
(a) Scatter plot comparing AUC scores of HDRNet with other machine learning algorithms.(b) Comparative analysis of the overall dynamic prediction performance between HDRNet and other machine learning methods (n=68 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; Wilcox test).Source data are provided as a Source Data file.

2 .
(a) Overall results of HDRNet using different structure information.HDRNet with icSHAPE performs best in both static and dynamic prediction tasks (n=68 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; Dunn's test).(b) The latent embedding of the learned features by HDRNet using different types of input structure features.(c) Left: The high attention binding region identified by HDRNet with different input structure characterization.Right: The structural models predicted by RNAfold with the corresponding structural technology scores as constraints.Source data are provided as a Source Data file.
Supplementary Fig. 6.(a) Performance comparison of HDRNet with different hierarchical structures.The results demonstrate that the proposed hierarchical network achieves superior performance in both static and dynamic prediction tasks.(n=68 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; T test.)(b) Feature correlation analysis of different HDRNet versions.Notably, HDRNet exhibits substantial feature correlation and feature hierarchy.Source data are provided as a Source Data file.

. 7 . 8 .
(a) Performance comparison of HDRNet and PrismNet through different cell lines (n=8, 17, 39, 84 and 112 in each group, with mean ± SD).(b) Performance gap between HDRNet and PrismNet through HEK293, HepG2 and K562 cell lines (n=17, 22 and 21 in each group, with mean ± SD).(c) Identified subgroups of RBP datasets in HEK293 cell line.(d) The integrative motifs identified by HDRNet on the RBPs within different subgroups.The top half presents the high-attention 6-mer fragment identified by HDRNet most frequently, while the lower half displays the structural motifs of the 6-mer sequence, where 'P' stands for paired, and 'U' indicates unpaired.(e) Heatmap representing the relative content of each 3-mer in the RBP binding site.RBPs with similar relative contents were grouped into a cluster by hierarchical clustering.Source data are provided as a Source Data file.HaoRan Zhu et al.(a) Bar chart of 6-mer contents for FMR1 and FXR2 across different cell lines.(b) Statistics of the most significant 6-mer regions identified by HDRNet on the FMR1 and FXR2 datasets.(c) Visualization of the salient map detected by HDRNet, highlighting specific binding regions within the FMR1 and FXR2 datasets.(d) Visualization of attention distribution using dynamic global contextual embedding, with attention concentrated in A/G-rich regions.Source data are provided as a Source Data file.HaoRan Zhu et al. | HDRNet bioRχiv | 13

INDEX. 10 .
Prediction Performance on RBPs with low expression level in K562 cell line Prediction Performance on RBPs with low expression level in K562 cell line Prediction Performance on RBPs with low expression level in HepG2 cell line Prediction Performance on RBPs with low expression level in HepG2 cell line a Prediction Performance on RBPs with high expression level in K562 cell line Prediction Performance on RBPs with high expression level in K562 cell line Prediction Performance on RBPs with high expression level in HepG2 cell line Prediction Performance on RBPs with high expression level in HepG2 cell line Dynamic Prediction Performance on RBPs with high expression level in both K562 and HepG2 cell line b Dynamic Prediction Performance on RBPs with low expression level in both K562 and HepG2 cell line Dynamic Prediction Performance on RBPs with low expression level in both K562 and HepG2 cell line Supplementary Fig. 9. (a) Static prediction performance comparison of HDRNet and baseline models on different expression levels of RBPs (top: n=54 in each group; bottom: n=42 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range).(b) The correlation between HDRNet's performance and RBP expression levels.(c) The correlation of RBP expression levels between K562 and HepG2 cell lines.(d) The performance comparison of HDRNet on RBPs with identical expression levels across cell lines.(top: n=25 in each group; bottom: n=31 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range) (e) The performance comparison of HDRNet on differentially expressed RBPs across cell lines.(top: n=3 in each group; bottom: n=3 in each group; center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range) (f) The correlation between RBP expression levels and HDRNet's dynamic prediction performance.Source data are provided as a Source Data file.(top: HepG2.expHepG2->K562 p=0.0152,HepG2.expK562->HepG2 p=0.0253; bottom: K562.expHepG2->K562 p=0.0187,K562.expK562->HepG2 p=0.0677;Pearson Correlation).HaoRan Zhu et al.(a) The performance comparison of HDRNet on RBPs with lower target expression levels.(b) The performance comparison of HDRNet on RBPs with higher target expression levels.(c) The comparison of HDRNet's dynamic prediction performance on RBPs with lower target expression levels.(d) The comparison of HDRNet's dynamic prediction performance on RBPs with higher target expression levels.Source data are provided as a Source Data file.

b
Supplementary Fig. 11.(a) The salient map of the high attention binding region captured by HDRNet.HDRNet successfully identifies the disease-related RNA repeats.(b) Performance comparison of DGCR8 and HNRNPU dynamic prediction, using the model trained on cell line data.(c) HDRNet identifies the CGG-rich region of DGCR8 binding patterns.Source data are provided as a Source Data file.INDEX 22).As illustrated in Supplementary Fig. 12b, HDRNet can dynamically distinguish these diseases-related RNA repeats sequences using models trained on different MBNL1 binding data, indicating that HDRNet can effectively extract RBP binding properties from the RBP binding data in different physiological environments.In summary, these results demonstrate the comprehensiveness of HDRNet in the task of dynamic prediction of RBP binding sites in multiple tissues.a Dynamic prediction based on model trained on 129Brain b chr15:80715413-80715514, Using C2C12 trained HDRNet chr15:80715413-80715514, Using 129Brain trained HDRNet chr4:28859047-28859148, Using C2C12 trained HDRNet chr4:28859047-28859148, Using 129Brain trained HDRNet CUG enriched region CCUG enriched region chr15:80715413-80715514 chr4:28859047-28859148 Supplementary Fig. 12.(a) Performance comparison of MBNL2 dynamic prediction performance using model trained on 129Brain date of mouse.(b) Salient binding regions identified by HDRNet under dynamic conditions.Source data are provided as a Source Data file.HaoRan Zhu et al. | HDRNet bioRχiv | 19

Supplementary Fig. 3. Ablation
comparison results of integrating RNA structure information from different methods.It can be observed that combining other features didn't further improve the performance of HDRNet (n=261 in each group, with mean ± SD).Source data are provided as a Source Data file. INDEXSupplementary

Note 4: Hierarchical Structure Improves the Prediction Performance and Fea- ture Correlation of HDRNet
(Muscleblind Like Splicing Regulator 1) in brain, heart, muscle, and myoblasts from mice (Wang et al. Cell 2012, PMID: 22901804) (22) from the CLIP-Seq data to further explore the performance of HDRNet in different contexts.In particular, we collected a total of five datasets (GEO accession: GSE39911), including two from the brain (129Brain, B6Brain), one from muscle (B6Muscle), one from heart (B6Heart), and one from myoblasts (C2C12 cells), where 129 and B6 are individual mouse ID numbers.The experimental results, as illustrated in Supplementary Fig.12aand Fig.4e, demonstrate that HDRNet provided performance improvements compared to other baseline methods, resulting in an increase in AUC from 5% to 10%.Meanwhile, it is important to note that MBNL1 and MBNL2 are both members of the muscleblind-like (MBNL) protein family, and therefore MBNL1 shares similar binding patterns with MBNL2, as discussed previously, showing binding specificity to CUG and CCUG pathological expansions(20,

Table 2 .
List of the candidate drugs for neurological diseases (two-sided Fisher-Irwin exact test).Source data are provided as a Source Data file.C 17 H 12 ClF 3 N 2 O